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Discovering Underlying Plans Based on Shallow Models by Hankz Hankui Zhuo; Yantian Zha; Subbarao Kambhampati; Xin Tian is a Computer Science article available to read on EtoBox.

What is Discovering Underlying Plans Based on Shallow Models about?

Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or action models in hand. Previous approaches either discover plans by maximally “matching” observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing action models to best explain the observed actions, assuming that complete action models are available. In real-world applications, however, target plans are often not from plan libraries, and complete action models are often not available, since building complete sets of plans and complete action models are often difficult or expensive. In this article, we view plan libraries as corpora and learn vector representations of actions using the corpora; we then discover target plans based on the vector representations. Specifically, we propose two approaches, DUP and RNNPlanner, to discover target plans based on vector representations of actions. DUP explores the EM-style (Expectation Maximization) framework to capture local contexts of actions and discover target plans by optimizing the probability of target plans, while RNNPlanner aims to leverage long-short

Who reads Discovering Underlying Plans Based on Shallow Models?

Author

Hankz Hankui Zhuo; Yantian Zha; Subbarao Kambhampati; Xin Tian

Publisher

ACM

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)